Résumé: Online social networks play a major role in the spread of information at very large scale. A lot of efforts have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

Résumé: Online social networks play a major role in the spread of information at very large scale and it becomes essential to provide means to analyze this phenomenon. Analyzing information diffusion proves to be a challenging task since the raw data produced by users of these networks are a flood of ideas, recommendations, opinions, etc. The aim of this PhD work is to help in the understanding of this phenomenon. So far, our contributions are the following: (i) a survey of developments in the field; (ii) T-BaSIC, a graph-based model for information diffusion prediction; (iii) SONDY, an open source platform that helps understanding social network users' interests and activity by providing emerging topics and events detection as well as network analysis functionalities.

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Résumé: Online social networks play a major role in the spread of information at very large scale and it becomes essential to provide means to analyse this phenomenon. In this paper we address the issue of predicting the temporal dynamics of the information diffusion process. We develop a graph-based approach built on the assumption that the macroscopic dynamics of the spreading process are explained by the topology of the network and the interactions that occur through it, between pairs of users, on the basis of properties at the microscopic level. We introduce a generic model, called T-BaSIC, and describe how to estimate its parameters from users behaviours using machine learning techniques. Contrary to classical approaches where the parameters are fixed in advance, T-BaSIC's parameters are functions depending of time, which permit to better approximate and adapt to the diffusion phenomenon observed in online social networks. Our proposal has been validated on real Twitter datasets. Experiments show that our approach is able to capture the particular patterns of diffusion depending of the studied sub-networks of users and topics. The results corroborate the "two-step" theory (1955) that states that information flows from media to a few "opinion leaders" who then transfer it to the mass population via social networks and show that it applies in the online context. This work also highlights interesting recommendations for future investigations.

Commentaires: Version mise à jour et étendue de l'article "A Predictive Model for the Temporal Dynamics of Information Diffusion in Online Social Networks"

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Résumé: This paper describes SONDY, a tool for analysis of trends and dynamics in online social network data. SONDY addresses two audiences: (i) end-users who want to explore social activity and (ii) researchers who want to experiment and compare mining techniques on social data. SONDY helps end-users like media analysts or journalists understand social network users interests and activity by providing emerging topics and events detection as well as network analysis functionalities. To this end, the application proposes visualizations such as interactive time-lines that summarize information and colored user graphs that reflect the structure of the network. SONDY also provides researchers an easy way to compare and evaluate recent techniques to mine social data, implement new algorithms and extend the application without being concerned with how to make it accessible.

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Résumé: Ce papier décrit la plateforme SONDY qui permet l'analyse et la fouille de données issues de réseaux sociaux en ligne. La plateforme permet d'explorer et de visualiser l'évolution des thématiques populaires et la structure du réseau social de manière interactive. Elle permet également de comparer les méthodes utilisées et d'en intégrer de nouvelles. La démonstration consistera à expérimenter tous les services de SONDY sur des données issues de Twitter.

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